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Installation
Learn how to install PyCaret
PyCaret 3.0-rc is now available.
pip install --pre pycaret to try it. Check out this example Notebook.PyCaret is tested and supported on the following 64-bit systems:
- Python 3.6 – 3.8
- Python 3.9 for Ubuntu only
- Ubuntu 16.04 or later
- Windows 7 or later
Install PyCaret with Python's pip package manager.
pip install pycaret
To install the full version (see dependencies below):
pip install pycaret[full]
If you want to try our nightly build (unstable) you can install pycaret-nightly from pip.
pip install pycaret-nightlyIn order to avoid potential conflicts with other packages, it is strongly recommended to use a virtual environment, e.g. python3 virtualenv (see python3 virtualenv documentation) or conda environments. Using an isolated environment makes it possible to install a specific version of pycaret and its dependencies independently of any previously installed Python packages.
# create a conda environment
conda create --name yourenvname python=3.8
# activate conda environment
conda activate yourenvname
# install pycaret
pip install pycaret
# create notebook kernel
python -m ipykernel install --user --name yourenvname --display-name "display-name"
PyCaret is not yet compatible with sklearn>=0.23.2.
With PyCaret, you can train models on GPU and speed up your workflow by 10x. To train models on GPU simply pass
use_gpu = True in the setup function. There is no change in the use of the API, however, in some cases, additional libraries have to be installed as they are not installed with the default version or the full version. As of the latest release, the following models can be trained on GPU:- Extreme Gradient Boosting (requires no further installation)
- Catboost (requires no further installation)
- Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires cuML >= 0.15
requirements
requirements-optional
requirements-test
pandas
scipy<=1.5.4
seaborn
matplotlib
IPython
joblib
scikit-learn==0.23.2
ipywidgets
yellowbrick>=1.0.1
lightgbm>=2.3.1
plotly>=4.4.1
wordcloud
textblob
cufflinks>=0.17.0
umap-learn
pyLDAvis
gensim<4.0.0
spacy<2.4.0
nltk
mlxtend>=0.17.0
pyod
pandas-profiling>=2.8.0
kmodes>=0.10.1
mlflow
imbalanced-learn==0.7.0
scikit-plot
Boruta
pyyaml<6.0.0
numba<0.55
shap
interpret<=0.2.4
tune-sklearn>=0.2.1
ray[tune]>=1.0.0
hyperopt
optuna>=2.2.0
scikit-optimize>=0.8.1
psutil
catboost>=0.23.2
xgboost>=1.1.0
explainerdashboard
m2cgen
evidently
autoviz
fairlearn
fastapi
uvicorn
gradio
fugue>=0.6.5
boto3
azure-storage-blob
google-cloud-storage
pytest
moto
codecov
NOTE: We are actively working on reducing default dependencies in the next major release. We intend to support functionality level and module-specific install in the future. For example:
pip install pycaret[nlp].To install the package directly from GitHub (latest source), use the following command:
pip install git+https://github.com/pycaret/pycaret.git#egg=pycaret
Don't forget to include the
#egg=pycaret part to explicitly name the project, this way pip can track metadata for it without having to have run the setup.py script.To launch the test suite, run the following command from outside the source directory:
pytest pycaret
Docker uses containers to create virtual environments that isolate a PyCaret installation from the rest of the system. PyCaret docker comes pre-installed with a Notebook environment. that can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The PyCaret Docker images are tested for each release.
docker run -p 8888:8888 pycaret/slim
For docker image with full version:
docker run -p 8888:8888 pycaret/full
Last modified 5mo ago